Developing Data Products Final Project

Sofia Perez
19/04/2021

Car Performance Analizer App

A shiny app was created to analise and predict car performance based on the mtcars dataset. The application has two main sections:

  1. Data Analysis

    • Allows user to customize a scatterplot based on the dataset.
    • Explains and calculates which is the best linear regression model for this dataset.

  2. Prediction

    • Allows user to introduce car attributes and calculates a performance prediction.

mtcars Dataset

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). A data frame with 32 observations on 11 (numeric) variables.

  • [, 1] mpg Miles/(US) gallon
  • [, 2] cyl Number of cylinders
  • [, 3] disp Displacement (cu.in.)
  • [, 4] hp Gross horsepower
  • [, 5] drat Rear axle ratio
  • [, 6] wt Weight (1000 lbs)
  • [, 7] qsec ¼ mile time
  • [, 8] vs Engine (0 = V-shaped, 1 = straight)
  • [, 9] am Transmission (0 = automatic, 1 = manual)
  • [,10] gear Number of forward gears
  • [,11] carb Number of carburetors
data(mtcars)
head(mtcars,3)

               mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

Example Plot

shiny::includeHTML("./Project Presentation-figure/plot_ly.html")

<!–html_preserve–><!DOCTYPE html>

<!–/html_preserve–>

Example Prediction Calculation

The following calculations were performed in order to predict car performance accourding to user input:

library(caret)
data<-mtcars[,c(1,2,4,6,9)]
row.names(data)<-c()
inTrain  <- createDataPartition(data$mpg, p=0.7, list=FALSE)
training <- data[inTrain, ]
testing  <- data[-inTrain, ]
set.seed(12345)
control <- trainControl(method="cv", number=3, verboseIter=FALSE)
Mod_RF <- train(mpg ~ cyl + hp + wt + am, data=training, method="rf", trControl=control)
Mod_RF$finalModel


Call:
 randomForest(x = x, y = y, mtry = param$mtry) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 4

          Mean of squared residuals: 6.287823
                    % Var explained: 80.78

user_input<-data.frame(3500,200,4,0)
colnames(user_input)<-c("wt","hp","cyl","am")
predict(Mod_RF, user_input)

      1 
15.6056